There have been several studies demonstrating the biases inherent to the RNA-Seq method as well as variation in results across protocols and platforms. Researchers have set about innovating methods to correct for these biases and variances, but until now, most correction methods involve the use of bioinformatics models for partial correction. (See Post – Bias Detection and Correction in RNA-Sequencing Data)
Recently, researchers at the NIH, the NIST, and Cold Spring Harbor Lab have developed a synthetic spike-in standard as another tool for combating biases. The spike-in control consists of a pool of 96 synthetic RNAs with various lengths, and GC content covering a 220 concentration range as spike-in controls to measure sensitivity, accuracy, and biases in RNA-seq experiments as well as to derive standard curves for quantifying the abundance of transcripts.
Using data collected as part of the ENCODE and modENCODE projects, they demonstrate that external RNA controls are a useful resource for evaluating sensitivity and accuracy of RNA-seq experiments for transcriptome discovery and quantification. These quality metrics facilitate comparable analysis across different samples, protocols, and platforms.
Jiang L, Schlesinger F, Davis CA, Zhang Y, Li R, Salit M, Gingeras TR, Oliver B. (2011) Synthetic spike-in standards for RNA-seq experiments. Genome Res [Epub ahead of print]. [abstract]